Smart Systems and Collaborative Innovation Networks for Productivity Improvement in SMEs

The adoption of Smart Manufacturing Systems in manufacturing companies is often seen as a strategy towards achieving improvements in productivity. However, there is little evidence to indicate that UK manufacturing SMEs are prepared for the implementation of such systems. Through the employment of a triangulation research approach involving the detailed examination of 36 UK manufacturing SMEs from three manufacturing sectors, this study investigates the level of awareness and understanding within SMEs of Smart Manufacturing Systems. The development of a profiling tool is shown and is subsequently used to audit company awareness and understanding of the key technologies, collaborative networks and systems of SMS. Further information obtained from semi-structured interviews and observations of manufacturing operations provide further contextual information. The findings indicate that whilst the priority technologies and systems differ between manufacturing sectors, the key issues around the need for developing appropriate collaborative networks and knowledge management systems are common to all sectors

Teaching and Learning with Technology During the COVID-19 Pandemic: Highlighting the Need for Micro-Meso-Macro Alignments

All over the world teaching and learning transitioned to forms of online education due to the COVID-19 pandemic. In this contribution, we recognize challenges that this disruptive change brought about for teachers and learners. We reflect on these challenges, based on discussions at EDUsummIT2019 in Quebec about the theme “Learners and learning contexts: New alignments for the digital age”. Informed by theoretical conceptualization and empirical evidence we identify micro-meso-macro alignments that need to be in place to move education into the digital age: alignments for quality learning contexts, alignments in support for teachers, and alignments through partnerships

READINESS OF LATVIA’S ORGANIZATIONS FOR ADVANCED ANALYTICS

The advanced analytics is one of the core tools to provide competitive advantage, sustainable development and foster productivity of the organization. Digital transformation and advanced analytics are two key trends in the emerging age of data, analytics, and automation. Digital transformation is the process of transforming how businesses operate when faced with digital disruption. Advanced analytics is the application of predictive and prescriptive models to analyse large, complex datasets in order to make critical business decisions. The focus of the paper is to assess the maturity level of advanced analytics in the organizations of Latvia by region, size and industry. Assessment was done by several domains like Organization, People, Data, Analytics, Technologies. The quantitative online survey was performed to assess the readiness of Latvia’s organizations for advanced analytics. The questionnaire was developed based on an academic literature review, reports and publications by researchers, analytical sector, industry experts and Author’s professionals experience in advanced analytics industry. The overall readiness level of Latvia’s organizations is 2.4 in 5 points scale. It differs by region, size of the organization and industry. Most of organizations do not have Analytics strategy, majority use spreadsheets based analytical tools, half of organizations use mostly only internal data, more than third part of organizations do not have any analytical resources. It leads to conclusion that majority of Latvia’s organizations are far from ability to improve productivity, be able to maximize the potential of the digital environment, to exploit data to make data-driven and automated decisions and are far from 21st century digital opportunities. Thus, puts under danger the sustainability of the organizations itself.

3T Framework for AI Adoption in Human Resource Management: A Strategic Assessment Tool of Talent, Trust, and Technology

Artificial intelligence (AI) is steadily entering and transforming the management, work, and organizational ecosystems. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. AI applications are increasingly assisting also Human Re-source Management (HRM) in undertaking time-critical tasks and managerial and administrative decision-making. However, more in-depth and comprehensive studies are needed to understand the specific factors affecting the full adoption of AI technology from a multi-level viewpoint and address the potential limitations of AI appropriation or its adverse outcomes in HRM.The purpose of this study is to investigate the conditions in which human talent may take advantage of the unique opportunities offered by AI. However, whereas previous studies were conducted on the individual perception of AI and technology readiness or adoption, an integrated approach aiming to combine talent management-related dimensions and managerial-related dimensions is still not avail-able. For this research gap, we build a strategic management assessment frame-work of the driving factors of Talent, Trust, and Technology (3T) in AI adoption in HRM. We investigate the impact of these trends on the human-related and technology ecosystems and provide an integrated analysis of individual micro (talent management) organizational macro (trust and technology) adoption of AI technology.The paper advances the current definition and understanding of individual human facilitators and impediments behind the ability to speed up the adoption of AI-based technology. The practical contribution can facilitate the human-centered and trustworthy design and adoption of AI

Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors

YesThis research highlights a contextual application for big data within a HR case study setting. This is achieved through the development of a normative conceptual model that seeks to envelop employee behaviors and attitudes in the context of organizational change readiness. This empirical application considers a data sample from a large public sector organization and through applying Structural Equation Modelling (SEM) identifies salary, job promotion, organizational loyalty and organizational identity influences on employee job satisfaction (suggesting and mediating employee readiness for organizational change). However in considering this specific context, the authors highlight how, where and why such a normative approach to employee factors may be limited and thus, proposes through a framework which brings together big data principles, implementation approaches and management commitment requirements can be applied and harnessed more effectively in order to assess employee attitudes and behaviors as part of wider HR predictive analytics (HRPA) approaches. The researchers conclude with a discussion on these research elements and a set of practical, conceptual and management implications of the findings along with recommendations for future research in the area

Supporting community engagement through teaching, student projects and research

The Education Acts statutory obligations for ITPs are not supported by the Crown funding model. Part of the statutory role of an ITP is “... promotes community learning and by research, particularly applied and technological research ...” [The education act 1989]. In relation to this a 2017 TEC report highlighted impaired business models and an excessive administrative burden as restrictive and impeding success. Further restrictions are seen when considering ITPs attract < 3 % of the available TEC funding for research, and ~ 20 % available TEC funding for teaching, despite having overall student efts of ~ 26 % nationally. An attempt to improve performance and engage through collaboration (community, industry, tertiary) at our institution is proving successful. The cross-disciplinary approach provides students high level experience and the technical stretch needed to be successful engineers, technologists and technicians. This study presents one of the methods we use to collaborate externally through teaching, student projects and research

Assessing Organizational Readiness for Data-driven Innovation: A Review of Literature

The growing demand for data has provided many opportunities for organizations to launch data-driven innovation (DDI) initiatives. DDI enables organizations to continuously respond to market opportunities and challenges and thereby sustain competitive advantage. However, many organizations fail in their attempt to implement DDI due to poor organizational readiness. This study investigates key factors that assist organizations in assessing their readiness for DDI. An extensive examination of literature was performed to identify readiness factors. The results highlighted nine organizational readiness factors for DDI based on the theoretical foundations of Technology-Organization and Environment framework and organizational readiness theory. The findings of this study contribute to the growing body of DDI literature and provide insights for organizations interested in implementing DDI initiatives